Our network is supervised on the inverse \(l_1\)  loss between the predicted depths \(\lbrace D_{t}\rbrace _{i=1}^{T}\)  and the ground truth \(D_{gt}\) . The loss is evaluated over the valid pixels
which have ground truth depths. Following the exponentially increasing weights as in [1]}, [2]}, the loss is defined as
\(\mathcal {L} = \sum _{t=1}^{T} \gamma ^{T-t} \frac{1}{N_v} \sum _{i=0}^{N_v-1} \Vert  \frac{1}{ D_t(i)} - \frac{1}{D_{gt}(i)} \Vert _1\) 
